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Dr-AliGomaa
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Model Details
Model Provider
Dr-AliGomaa
Model Tree
openai/whisper-large-v3
Input Modalities
Output Modalities
Supported Functionality
Identical to whisper-large-v3-ar-eg — timestamp supervision costs no accuracy.
Normalized WER/CER (%), scored with
eval/asr_score.py.
| Evaluation | WER | CER |
|---|---|---|
| Quran (ʿAbd al-Bāsiṭ, 7,280 clips) | 0.50 | 0.14 |
| Hadith (Bukhari + Muslim, 4,752 clips) | 3.71 | 1.08 |
Egyptian — ar-eg-dataset validation, same speaker/register | 5.41 | 1.76 |
| Egyptian — lahgtna-v3, zero-shot spontaneous | 17.20 | 6.32 |
Text accuracy is measured with return_timestamps=False; the timestamps are an additional
output, not a different transcript.
This is the one place where usage differs from the other two models.
python
import torchfrom transformers import pipelinepipe = pipeline("automatic-speech-recognition",model="Dr-AliGomaa/whisper-large-v3-ar-eg-timestamps",torch_dtype=torch.float16,device="cuda:0",chunk_length_s=30,)gen = {"language": "arabic","task": "transcribe","num_beams": 5,"temperature": (0.2),"condition_on_prev_tokens": False,"compression_ratio_threshold": 1.35,"logprob_threshold": -1.0,"max_new_tokens": 444,}out = pipe("lecture.mp3", return_timestamps="word", generate_kwargs=gen)for ch in out["chunks"][:10]:start, end = ch["timestamp"]print(f"{start:7.2f} → {end:7.2f} {ch['text']}")
python
out = pipe("lecture.mp3", return_timestamps=True, generate_kwargs=gen)
Segments can run 15–20 seconds long. Whisper emits a segment boundary where it decides a phrase ended, and on recitation — which is continuous, melodic and sparsely punctuated — those decisions land far apart. If you need sentence- or verse-level alignment, do not ask for segment timestamps. Ask for word timestamps and group them yourself; you control the rule and the result is far tighter.
python
def group_words(chunks, max_gap=0.6, max_dur=12.0):"""Word chunks -> sentence-ish spans. Break on a silence gap or on length."""spans, cur = [], Nonefor ch in chunks:start, end = ch["timestamp"]if start is None or end is None: # dropped word, keep the textif cur: cur["text"] += ch["text"]continueif cur is None:cur = {"start": start, "end": end, "text": ch["text"]}continuegap = start - cur["end"]if gap > max_gap or (end - cur["start"]) > max_dur:spans.append(cur)cur = {"start": start, "end": end, "text": ch["text"]}else:cur["end"] = endcur["text"] += ch["text"]if cur: spans.append(cur)return spansfor s in group_words(out["chunks"]):print(f"[{s['start']:.2f}–{s['end']:.2f}] {s['text'].strip()}")
Tune max_gap to the material: recitation pauses between verses are long, so 0.6–1.0 s works
well; conversational speech needs a smaller value.
timestamp = (None, None) if the model dropped the alignment —
keep the text, as the snippet above does, rather than dropping the word.return_timestamps=False and you get exactly
the behaviour of whisper-large-v3-ar-eg.1. Run the audio pipeline first — silence-trim, loudness-normalize to −16 LUFS, edge-pad
100 ms, gain-correct, segment ≤ 30 s. This matters doubly here: timestamps are relative to
the audio you feed in, so trimming silence after transcription shifts every one of them.
pipeline/.
2. Score with eval/asr_score.py — Arabic WER moves materially with the normalizer.
| Base | openai/whisper-large-v3 |
| Trained on | the Egyptian mix + timestamp supervision on 23.4 % of rows |
| Held out | Quran, Hadith, Egyptian (10 h), plus timestamps |
| Timestamp routing | a predict_timestamps flag sends each example to the timestamped or plain decoder prefix, so both live in one mix |
| Batch | 4 per device × 8 GPUs = 32 effective |
| Precision / distributed | bf16 + tf32, DeepSpeed ZeRO |
Full recipe: training/training.py.
bibtex
@misc{kotb2026quranhadith,title = {A Quran and Hadith Speech Resource and Benchmark for Arabic ASR,with Professional-Reciter Training and Validation},author = {Mohamed Kotb},year = {2026},publisher = {Zenodo},doi = {10.5281/zenodo.21927416},url = {https://doi.org/10.5281/zenodo.21927416},note = {Preprint}}